TY - GEN
T1 - Detection Character Regions and Comparison Features in Event flyer Images based on Machine Learning
AU - Orimoto, Ken
AU - Tateyama, Tomoko
AU - Honda, Masashi
AU - Miyamoto, Takumi
AU - Matsumoto, Shimpei
N1 - Funding Information:
The authors would like to thank Y.Shimizu and K.Okimoto from TamemaInc. who develop web application system ”tame-map”. They Provided many event flyer images thought the system and cooperation of advice for our experiments. This work is supported in part by the grand in aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (Mext) under the Grant No.18K11454 and 18K02214, Furukawa technical fundamation, and Intelligent Visual Analytics Research Center in Hiroshima Institute of Technology.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - Tame-map is a smartphone application that allows users to easily share regional events information within their daily lives by uploading digital flyer images on the web. Although there are a lot of event flyer images uploaded daily, these data have been organized manually. This manual work is hard and takes a huge time, so a system to automate the manual work has been required. An automatic processing method will support the organization, such as categorization, assigning keywords to each image. To develop a basis of automatic flyer images processing, this paper examines detection character region in event flyer images based on machine learning. Concretely, we compared the two features which are obtained vectors based on unsupervised/supervised leanings, such as k-means and SVM respectively.
AB - Tame-map is a smartphone application that allows users to easily share regional events information within their daily lives by uploading digital flyer images on the web. Although there are a lot of event flyer images uploaded daily, these data have been organized manually. This manual work is hard and takes a huge time, so a system to automate the manual work has been required. An automatic processing method will support the organization, such as categorization, assigning keywords to each image. To develop a basis of automatic flyer images processing, this paper examines detection character region in event flyer images based on machine learning. Concretely, we compared the two features which are obtained vectors based on unsupervised/supervised leanings, such as k-means and SVM respectively.
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U2 - 10.1109/IIAI-AAI50415.2020.00125
DO - 10.1109/IIAI-AAI50415.2020.00125
M3 - Conference contribution
AN - SCOPUS:85107183130
T3 - Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
SP - 602
EP - 607
BT - Proceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
A2 - Matsuo, Tokuro
A2 - Takamatsu, Kunihiko
A2 - Ono, Yuichi
A2 - Hirokawa, Sachio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
Y2 - 1 September 2020 through 15 September 2020
ER -